Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis

Abstract

The machine learning community adopted the use of null
hypothesis significance testing (NHST) in order to ensure the
statistical validity of results. Many scientific fields however
realized the shortcomings of frequentist reasoning and in the
most radical cases even banned its use in publications. We
should do the same: just as we have embraced the Bayesian
paradigm in the development of new machine learning methods, so
we should also use it in the analysis of our own results. We
argue for abandonment of NHST by exposing its fallacies and,
more importantly, offer better---more sound and useful---
alternatives for it.